{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUC Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import metrics\n",
    "from itertools import cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0    1\n",
      "0    5.1  3.5\n",
      "1    4.9  3.0\n",
      "2    4.7  3.2\n",
      "3    4.6  3.1\n",
      "4    5.0  3.6\n",
      "5    5.4  3.9\n",
      "6    4.6  3.4\n",
      "7    5.0  3.4\n",
      "8    4.4  2.9\n",
      "9    4.9  3.1\n",
      "10   5.4  3.7\n",
      "11   4.8  3.4\n",
      "12   4.8  3.0\n",
      "13   4.3  3.0\n",
      "14   5.8  4.0\n",
      "15   5.7  4.4\n",
      "16   5.4  3.9\n",
      "17   5.1  3.5\n",
      "18   5.7  3.8\n",
      "19   5.1  3.8\n",
      "20   5.4  3.4\n",
      "21   5.1  3.7\n",
      "22   4.6  3.6\n",
      "23   5.1  3.3\n",
      "24   4.8  3.4\n",
      "25   5.0  3.0\n",
      "26   5.0  3.4\n",
      "27   5.2  3.5\n",
      "28   5.2  3.4\n",
      "29   4.7  3.2\n",
      "..   ...  ...\n",
      "120  6.9  3.2\n",
      "121  5.6  2.8\n",
      "122  7.7  2.8\n",
      "123  6.3  2.7\n",
      "124  6.7  3.3\n",
      "125  7.2  3.2\n",
      "126  6.2  2.8\n",
      "127  6.1  3.0\n",
      "128  6.4  2.8\n",
      "129  7.2  3.0\n",
      "130  7.4  2.8\n",
      "131  7.9  3.8\n",
      "132  6.4  2.8\n",
      "133  6.3  2.8\n",
      "134  6.1  2.6\n",
      "135  7.7  3.0\n",
      "136  6.3  3.4\n",
      "137  6.4  3.1\n",
      "138  6.0  3.0\n",
      "139  6.9  3.1\n",
      "140  6.7  3.1\n",
      "141  6.9  3.1\n",
      "142  5.8  2.7\n",
      "143  6.8  3.2\n",
      "144  6.7  3.3\n",
      "145  6.7  3.0\n",
      "146  6.3  2.5\n",
      "147  6.5  3.0\n",
      "148  6.2  3.4\n",
      "149  5.9  3.0\n",
      "\n",
      "[150 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "pd.set_option('display.width', 300)\n",
    "np.set_printoptions(suppress=True)\n",
    "data = pd.read_csv('iris.data', header=None)\n",
    "iris_types = data[4].unique()\n",
    "for i, iris_type in enumerate(iris_types):\n",
    "    data.set_value(data[4] == iris_type, 4, i)\n",
    "x = data.iloc[:, :2]\n",
    "n, features = x.shape\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.iloc[:, -1].astype(np.int)\n",
    "c_number = np.unique(y).size\n",
    "x, x_test, y, y_test = train_test_split(x, y, train_size=0.6, test_size=0.4, random_state=0)\n",
    "y_one_hot = label_binarize(y_test, classes=np.arange(c_number))\n",
    "alpha = np.logspace(-2, 2, 20)\n",
    "models = [\n",
    "    ['KNN', KNeighborsClassifier(n_neighbors=7)],\n",
    "    ['LogisticRegression', LogisticRegressionCV(Cs=alpha, penalty='l2', cv=3)],\n",
    "    ['SVM(Linear)', GridSearchCV(SVC(kernel='linear', decision_function_shape='ovr'), param_grid={'C': alpha})],\n",
    "    ['SVM(RBF)', GridSearchCV(SVC(kernel='rbf', decision_function_shape='ovr'), param_grid={'C': alpha, 'gamma': alpha})]]\n",
    "colors = cycle('gmcr')\n",
    "mpl.rcParams['font.sans-serif'] = u'SimHei'\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.877083333333\n",
      "[ 2.06913808  2.06913808  0.11288379]\n",
      "0.890972222222\n",
      "{'C': 0.11288378916846889}\n",
      "0.880694444444\n",
      "{'C': 0.18329807108324356, 'gamma': 0.48329302385717521}\n",
      "0.892361111111\n"
     ]
    },
    {
     "data": {
      "image/png": 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efPNNPvzwQyZPnozRaGT06NFSQ4i9e/eyZMkS6b1Op2Pv3r1O+RQVFTFt2jTOnTvHN998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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25f65b51f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 6), facecolor='w')\n",
    "for (name, model), color in zip(models, colors):\n",
    "    model.fit(x, y)\n",
    "    if hasattr(model, 'C_'):\n",
    "        print(model.C_)\n",
    "    if hasattr(model, 'best_params_'):\n",
    "        print(model.best_params_)\n",
    "    if hasattr(model, 'predict_proba'):\n",
    "        y_score = model.predict_proba(x_test)\n",
    "    else:\n",
    "        y_score = model.decision_function(x_test)\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_one_hot.ravel(), y_score.ravel())\n",
    "    auc = metrics.auc(fpr, tpr)\n",
    "    print(auc)\n",
    "    plt.plot(fpr, tpr, c=color, lw=2, alpha=0.7, label=u'%s，AUC=%.3f' % (name, auc))\n",
    "plt.plot((0, 1), (0, 1), c='#808080', lw=2, ls='--', alpha=0.7)\n",
    "plt.xlim((-0.01, 1.02))\n",
    "plt.ylim((-0.01, 1.02))\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.grid(b=True, ls=':')\n",
    "plt.legend(loc='lower right', fancybox=True, framealpha=0.8, fontsize=12)\n",
    "# plt.legend(loc='lower right', fancybox=True, framealpha=0.8, edgecolor='#303030', fontsize=12)\n",
    "plt.title(u'鸢尾花数据不同分类器的ROC和AUC', fontsize=17)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
